# """ BEiT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)

# Model from official source: https://github.com/microsoft/unilm/tree/master/beit

# @inproceedings{beit,
# title={{BEiT}: {BERT} Pre-Training of Image Transformers},
# author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei},
# booktitle={International Conference on Learning Representations},
# year={2022},
# url={https://openreview.net/forum?id=p-BhZSz59o4}
# }

# BEiT-v2 from https://github.com/microsoft/unilm/tree/master/beit2

# @article{beitv2,
# title={{BEiT v2}: Masked Image Modeling with Vector-Quantized Visual Tokenizers},
# author={Zhiliang Peng and Li Dong and Hangbo Bao and Qixiang Ye and Furu Wei},
# year={2022},
# eprint={2208.06366},
# archivePrefix={arXiv},
# primaryClass={cs.CV}
# }

# At this point only the 1k fine-tuned classification weights and model configs have been added,
# see original source above for pre-training models and procedure.

# Modifications by / Copyright 2021 Ross Wightman, original copyrights below
# """
# # --------------------------------------------------------
# # BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# # Github source: https://github.com/microsoft/unilm/tree/master/beit
# # Copyright (c) 2021 Microsoft
# # Licensed under The MIT License [see LICENSE for details]
# # By Hangbo Bao
# # Based on timm and DeiT code bases
# # https://github.com/rwightman/pytorch-image-models/tree/master/timm
# # https://github.com/facebookresearch/deit/
# # https://github.com/facebookresearch/dino
# # --------------------------------------------------------'
# # pylint: disable=use-dict-literal
# import math
# from typing import Callable, List, Optional, Tuple, Union

# import mindspore
# from mindnlp.core import nn, ops
# from mindnlp.core.nn import functional as F

# from mindnlp.configs import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
# from mindnlp.mimm.layers import PatchEmbed, Mlp, SwiGLU, LayerNorm, DropPath, trunc_normal_
# from mindnlp.mimm.layers import resample_patch_embed, resample_abs_pos_embed, resize_rel_pos_bias_table, ndgrid


# from ._builder import build_model_with_cfg
# from ._features import feature_take_indices
# from ._registry import generate_default_cfgs, register_model

# __all__ = ['Beit']


# def gen_relative_position_index(window_size: Tuple[int, int]) -> mindspore.Tensor:
#     num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
#     # cls to token & token 2 cls & cls to cls
#     # get pair-wise relative position index for each token inside the window
#     window_area = window_size[0] * window_size[1]
#     coords = ops.stack(ndgrid(ops.arange(window_size[0]), ops.arange(window_size[1])))  # 2, Wh, Ww
#     coords_flatten = ops.flatten(coords, 1)  # 2, Wh*Ww
#     relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
#     relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
#     relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
#     relative_coords[:, :, 1] += window_size[1] - 1
#     relative_coords[:, :, 0] *= 2 * window_size[1] - 1
#     relative_position_index = ops.zeros((window_area + 1,) * 2, dtype=relative_coords.dtype)
#     relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
#     relative_position_index[0, 0:] = num_relative_distance - 3
#     relative_position_index[0:, 0] = num_relative_distance - 2
#     relative_position_index[0, 0] = num_relative_distance - 1
#     return relative_position_index


# class Attention(nn.Module):
#     fused_attn: bool

#     def __init__(
#             self,
#             dim: int,
#             num_heads: int = 8,
#             qkv_bias: bool = False,
#             qkv_bias_separate: bool = False,
#             attn_drop: float = 0.,
#             proj_drop: float = 0.,
#             window_size: Optional[Tuple[int, int]] = None,
#             attn_head_dim: Optional[int] = None,
#     ):
#         super().__init__()
#         self.num_heads = num_heads
#         head_dim = dim // num_heads
#         if attn_head_dim is not None:
#             head_dim = attn_head_dim
#         all_head_dim = head_dim * self.num_heads
#         self.scale = head_dim ** -0.5
#         self.qkv_bias_separate = qkv_bias_separate

#         self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
#         if qkv_bias:
#             self.q_bias = nn.Parameter(ops.zeros(all_head_dim))
#             self.register_buffer('k_bias', ops.zeros(all_head_dim), persistent=False)
#             self.v_bias = nn.Parameter(ops.zeros(all_head_dim))
#         else:
#             self.q_bias = None
#             self.k_bias = None
#             self.v_bias = None

#         if window_size:
#             self.window_size = window_size
#             self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
#             self.relative_position_bias_table = nn.Parameter(
#                 ops.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
#             self.register_buffer("relative_position_index", gen_relative_position_index(window_size), persistent=False)
#         else:
#             self.window_size = None
#             self.relative_position_bias_table = None
#             self.relative_position_index = None

#         self.attn_drop = nn.Dropout(attn_drop)
#         self.proj = nn.Linear(all_head_dim, dim)
#         self.proj_drop = nn.Dropout(proj_drop)

#     def _get_rel_pos_bias(self):
#         relative_position_bias = self.relative_position_bias_table[
#             self.relative_position_index.view(-1)].view(
#             self.window_size[0] * self.window_size[1] + 1,
#             self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
#         relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
#         return relative_position_bias.unsqueeze(0)

#     def forward(self, x, shared_rel_pos_bias: Optional[mindspore.Tensor] = None):
#         B, N, C = x.shape

#         if self.q_bias is None:
#             qkv = self.qkv(x)
#         else:
#             qkv_bias = ops.cat((self.q_bias, self.k_bias, self.v_bias))
#             if self.qkv_bias_separate:
#                 qkv = self.qkv(x)
#                 qkv += qkv_bias
#             else:
#                 qkv = F.linear(x, weight=self.qkv.weight, bias=qkv_bias)
#         qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
#         q, k, v = qkv.unbind(0)  # B, num_heads, N, head_dim

#         q = q * self.scale
#         attn = (q @ k.transpose(-2, -1))

#         if self.relative_position_bias_table is not None:
#             attn = attn + self._get_rel_pos_bias()
#         if shared_rel_pos_bias is not None:
#             attn = attn + shared_rel_pos_bias

#         attn = attn.softmax(dim=-1)
#         attn = self.attn_drop(attn)
#         x = attn @ v

#         x = x.transpose(1, 2).reshape(B, N, C)
#         x = self.proj(x)
#         x = self.proj_drop(x)
#         return x


# class Block(nn.Module):

#     def __init__(
#             self,
#             dim: int,
#             num_heads: int,
#             qkv_bias: bool = False,
#             mlp_ratio: float = 4.,
#             scale_mlp: bool = False,
#             swiglu_mlp: bool = False,
#             proj_drop: float = 0.,
#             attn_drop: float = 0.,
#             drop_path: float = 0.,
#             init_values: Optional[float] = None,
#             act_layer: Callable = nn.GELU,
#             norm_layer: Callable = LayerNorm,
#             window_size: Optional[Tuple[int, int]] = None,
#             attn_head_dim: Optional[int] = None,
#     ):
#         super().__init__()
#         self.norm1 = norm_layer(dim)
#         self.attn = Attention(
#             dim,
#             num_heads=num_heads,
#             qkv_bias=qkv_bias,
#             attn_drop=attn_drop,
#             proj_drop=proj_drop,
#             window_size=window_size,
#             attn_head_dim=attn_head_dim,
#         )
#         # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
#         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

#         self.norm2 = norm_layer(dim)
#         if swiglu_mlp:
#             self.mlp = SwiGLU(
#                 in_features=dim,
#                 hidden_features=int(dim * mlp_ratio),
#                 norm_layer=norm_layer if scale_mlp else None,
#                 drop=proj_drop,
#             )
#         else:
#             self.mlp = Mlp(
#                 in_features=dim,
#                 hidden_features=int(dim * mlp_ratio),
#                 act_layer=act_layer,
#                 norm_layer=norm_layer if scale_mlp else None,
#                 drop=proj_drop,
#             )
#         self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

#         if init_values:
#             self.gamma_1 = nn.Parameter(init_values * ops.ones(dim))
#             self.gamma_2 = nn.Parameter(init_values * ops.ones(dim))
#         else:
#             self.gamma_1, self.gamma_2 = None, None

#     def forward(self, x, shared_rel_pos_bias: Optional[mindspore.Tensor] = None):
#         if self.gamma_1 is None:
#             x = x + self.drop_path1(self.attn(self.norm1(x), shared_rel_pos_bias=shared_rel_pos_bias))
#             x = x + self.drop_path2(self.mlp(self.norm2(x)))
#         else:
#             x = x + self.drop_path1(self.gamma_1 * self.attn(self.norm1(x), shared_rel_pos_bias=shared_rel_pos_bias))
#             x = x + self.drop_path2(self.gamma_2 * self.mlp(self.norm2(x)))
#         return x


# class RelativePositionBias(nn.Module):

#     def __init__(self, window_size, num_heads):
#         super().__init__()
#         self.window_size = window_size
#         self.window_area = window_size[0] * window_size[1]
#         num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
#         self.relative_position_bias_table = nn.Parameter(ops.zeros(num_relative_distance, num_heads))
#         # trunc_normal_(self.relative_position_bias_table, std=.02)
#         self.register_buffer("relative_position_index", gen_relative_position_index(window_size))

#     def forward(self):
#         relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
#             self.window_area + 1, self.window_area + 1, -1)  # Wh*Ww,Wh*Ww,nH
#         return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww


# class Beit(nn.Module):
#     """ Vision Transformer with support for patch or hybrid CNN input stage
#     """

#     def __init__(
#             self,
#             img_size: Union[int, Tuple[int, int]] = 224,
#             patch_size: Union[int, Tuple[int, int]] = 16,
#             in_chans: int = 3,
#             num_classes: int = 1000,
#             global_pool: str = 'avg',
#             embed_dim: int = 768,
#             depth: int = 12,
#             num_heads: int = 12,
#             qkv_bias: bool = True,
#             mlp_ratio: float = 4.,
#             swiglu_mlp: bool = False,
#             scale_mlp: bool = False,
#             drop_rate: float = 0.,
#             pos_drop_rate: float = 0.,
#             proj_drop_rate: float = 0.,
#             attn_drop_rate: float = 0.,
#             drop_path_rate: float = 0.,
#             norm_layer: Callable = LayerNorm,
#             init_values: Optional[float] = None,
#             use_abs_pos_emb: bool = True,
#             use_rel_pos_bias: bool = False,
#             use_shared_rel_pos_bias: bool = False,
#             head_init_scale: float = 0.001,
#     ):
#         super().__init__()
#         self.num_classes = num_classes
#         self.global_pool = global_pool
#         self.num_features = self.head_hidden_size = self.embed_dim = embed_dim  # for consistency with other models
#         self.num_prefix_tokens = 1
#         self.grad_checkpointing = False

#         self.patch_embed = PatchEmbed(
#             img_size=img_size,
#             patch_size=patch_size,
#             in_chans=in_chans,
#             embed_dim=embed_dim,
#         )
#         num_patches = self.patch_embed.num_patches
#         r = self.patch_embed.feat_ratio() if hasattr(self.patch_embed, 'feat_ratio') else patch_size

#         self.cls_token = nn.Parameter(ops.zeros(1, 1, embed_dim))
#         # self.mask_token = nn.Parameter(ops.zeros(1, 1, embed_dim))
#         self.pos_embed = nn.Parameter(ops.zeros(1, num_patches + 1, embed_dim)) if use_abs_pos_emb else None
#         self.pos_drop = nn.Dropout(p=pos_drop_rate)

#         if use_shared_rel_pos_bias:
#             self.rel_pos_bias = RelativePositionBias(
#                 window_size=self.patch_embed.grid_size,
#                 num_heads=num_heads,
#             )
#         else:
#             self.rel_pos_bias = None

#         dpr = [x.item() for x in ops.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
#         self.blocks = nn.ModuleList([
#             Block(
#                 dim=embed_dim,
#                 num_heads=num_heads,
#                 qkv_bias=qkv_bias,
#                 mlp_ratio=mlp_ratio,
#                 scale_mlp=scale_mlp,
#                 swiglu_mlp=swiglu_mlp,
#                 proj_drop=proj_drop_rate,
#                 attn_drop=attn_drop_rate,
#                 drop_path=dpr[i],
#                 norm_layer=norm_layer,
#                 init_values=init_values,
#                 window_size=self.patch_embed.grid_size if use_rel_pos_bias else None,
#             )
#             for i in range(depth)])
#         self.feature_info = [
#             dict(module=f'blocks.{i}', num_chs=embed_dim, reduction=r) for i in range(depth)]

#         use_fc_norm = self.global_pool == 'avg'
#         self.norm = nn.Identity() if use_fc_norm else norm_layer(embed_dim)
#         self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
#         self.head_drop = nn.Dropout(drop_rate)
#         self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()

#         self.apply(self._init_weights)
#         if self.pos_embed is not None:
#             trunc_normal_(self.pos_embed, std=.02)
#         trunc_normal_(self.cls_token, std=.02)

#         self.fix_init_weight()
#         if isinstance(self.head, nn.Linear):
#             trunc_normal_(self.head.weight, std=.02)
#             self.head.weight.data.mul_(head_init_scale)
#             self.head.bias.data.mul_(head_init_scale)

#     def fix_init_weight(self):
#         def rescale(param, layer_id):
#             param.div_(math.sqrt(2.0 * layer_id))

#         for layer_id, layer in enumerate(self.blocks):
#             rescale(layer.attn.proj.weight.data, layer_id + 1)
#             rescale(layer.mlp.fc2.weight.data, layer_id + 1)

#     def _init_weights(self, m):
#         if isinstance(m, nn.Linear):
#             trunc_normal_(m.weight, std=.02)
#             if isinstance(m, nn.Linear) and m.bias is not None:
#                 nn.init.constant_(m.bias, 0)
#         elif isinstance(m, nn.LayerNorm):
#             nn.init.constant_(m.bias, 0)
#             nn.init.constant_(m.weight, 1.0)

#     def no_weight_decay(self):
#         nwd = {'pos_embed', 'cls_token'}
#         for n, _ in self.named_parameters():
#             if 'relative_position_bias_table' in n:
#                 nwd.add(n)
#         return nwd

#     def set_grad_checkpointing(self, enable=True):
#         self.grad_checkpointing = enable

#     def group_matcher(self, coarse=False):
#         matcher = dict(
#             stem=r'^cls_token|pos_embed|patch_embed|rel_pos_bias',  # stem and embed
#             blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))],
#         )
#         return matcher

#     def get_classifier(self) -> nn.Module:
#         return self.head

#     def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
#         self.num_classes = num_classes
#         if global_pool is not None:
#             self.global_pool = global_pool
#         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

#     def forward_intermediates(
#             self,
#             x: mindspore.Tensor,
#             indices: Optional[Union[int, List[int]]] = None,
#             return_prefix_tokens: bool = False,
#             norm: bool = False,
#             stop_early: bool = False,
#             output_fmt: str = 'NCHW',
#             intermediates_only: bool = False,
#     ) -> Union[List[mindspore.Tensor], Tuple[mindspore.Tensor, List[mindspore.Tensor]]]:
#         """ Forward features that returns intermediates.

#         Args:
#             x: Input image tensor
#             indices: Take last n blocks if an int, if is a sequence, select by matching indices
#             return_prefix_tokens: Return both prefix and spatial intermediate tokens
#             norm: Apply norm layer to all intermediates
#             stop_early: Stop iterating over blocks when last desired intermediate hit
#             output_fmt: Shape of intermediate feature outputs
#             intermediates_only: Only return intermediate features
#         Returns:

#         """
#         assert output_fmt in ('NCHW', 'NLC'), 'Output format must be one of NCHW or NLC.'
#         reshape = output_fmt == 'NCHW'
#         intermediates = []
#         take_indices, max_index = feature_take_indices(len(self.blocks), indices)

#         # forward pass
#         B, _, height, width = x.shape
#         x = self.patch_embed(x)
#         x = ops.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
#         if self.pos_embed is not None:
#             x = x + self.pos_embed
#         x = self.pos_drop(x)

#         rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
#         if not stop_early:  # can't slice blocks in torchscript
#             blocks = self.blocks
#         else:
#             blocks = self.blocks[:max_index + 1]
#         for i, blk in enumerate(blocks):
#             x = blk(x, shared_rel_pos_bias=rel_pos_bias)
#             if i in take_indices:
#                 # normalize intermediates with final norm layer if enabled
#                 intermediates.append(self.norm(x) if norm else x)

#         # process intermediates
#         if self.num_prefix_tokens:
#             # split prefix (e.g. class, distill) and spatial feature tokens
#             prefix_tokens = [y[:, 0:self.num_prefix_tokens] for y in intermediates]
#             intermediates = [y[:, self.num_prefix_tokens:] for y in intermediates]
#         if reshape:
#             # reshape to BCHW output format
#             H, W = self.patch_embed.dynamic_feat_size((height, width))
#             intermediates = [y.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for y in intermediates]

#         if intermediates_only:
#             return intermediates

#         x = self.norm(x)

#         return x, intermediates

#     def prune_intermediate_layers(
#             self,
#             indices: Union[int, List[int]] = 1,
#             prune_norm: bool = False,
#             prune_head: bool = True,
#     ):
#         """ Prune layers not required for specified intermediates.
#         """
#         take_indices, max_index = feature_take_indices(len(self.blocks), indices)
#         self.blocks = self.blocks[:max_index + 1]  # truncate blocks
#         if prune_norm:
#             self.norm = nn.Identity()
#         if prune_head:
#             self.fc_norm = nn.Identity()
#             self.reset_classifier(0, '')
#         return take_indices

#     def forward_features(self, x):
#         x = self.patch_embed(x)
#         x = ops.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
#         if self.pos_embed is not None:
#             x = x + self.pos_embed
#         x = self.pos_drop(x)

#         rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
#         for blk in self.blocks:
#             if self.grad_checkpointing:
#                 x = checkpoint(blk, x, shared_rel_pos_bias=rel_pos_bias)
#             else:
#                 x = blk(x, shared_rel_pos_bias=rel_pos_bias)
#         x = self.norm(x)
#         return x

#     def forward_head(self, x, pre_logits: bool = False):
#         if self.global_pool:
#             x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
#         x = self.fc_norm(x)
#         x = self.head_drop(x)
#         return x if pre_logits else self.head(x)

#     def forward(self, x):
#         x = self.forward_features(x)
#         x = self.forward_head(x)
#         return x


# def _cfg(url='', **kwargs):
#     return {
#         'url': url,
#         'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
#         'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
#         'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
#         'first_conv': 'patch_embed.proj', 'classifier': 'head',
#         **kwargs
#     }


# default_cfgs = generate_default_cfgs({
#     'beit_base_patch16_224.in22k_ft_in22k_in1k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
#         hf_hub_id='timm/'),
#     'beit_base_patch16_384.in22k_ft_in22k_in1k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_384_pt22k_ft22kto1k.pth',
#         hf_hub_id='timm/',
#         input_size=(3, 384, 384), crop_pct=1.0,
#     ),
#     'beit_base_patch16_224.in22k_ft_in22k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22k.pth',
#         hf_hub_id='timm/',
#         num_classes=21841,
#     ),
#     'beit_large_patch16_224.in22k_ft_in22k_in1k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth',
#         hf_hub_id='timm/'),
#     'beit_large_patch16_384.in22k_ft_in22k_in1k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_384_pt22k_ft22kto1k.pth',
#         hf_hub_id='timm/',
#         input_size=(3, 384, 384), crop_pct=1.0,
#     ),
#     'beit_large_patch16_512.in22k_ft_in22k_in1k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_512_pt22k_ft22kto1k.pth',
#         hf_hub_id='timm/',
#         input_size=(3, 512, 512), crop_pct=1.0,
#     ),
#     'beit_large_patch16_224.in22k_ft_in22k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth',
#         hf_hub_id='timm/',
#         num_classes=21841,
#     ),

#     'beitv2_base_patch16_224.in1k_ft_in22k_in1k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21kto1k.pth',
#         hf_hub_id='timm/',
#         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
#     ),
#     'beitv2_base_patch16_224.in1k_ft_in1k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft1k.pth',
#         hf_hub_id='timm/',
#         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
#     ),
#     'beitv2_base_patch16_224.in1k_ft_in22k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21k.pth',
#         hf_hub_id='timm/',
#         num_classes=21841, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
#     ),
#     'beitv2_large_patch16_224.in1k_ft_in22k_in1k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21kto1k.pth',
#         hf_hub_id='timm/',
#         crop_pct=0.95, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
#     ),
#     'beitv2_large_patch16_224.in1k_ft_in1k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft1k.pth',
#         hf_hub_id='timm/',
#         crop_pct=0.95, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
#     ),
#     'beitv2_large_patch16_224.in1k_ft_in22k': _cfg(
#         #url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21k.pth',
#         hf_hub_id='timm/',
#         num_classes=21841, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
#     ),
# })


# def checkpoint_filter_fn(state_dict, model, interpolation='bicubic', antialias=True):
#     state_dict = state_dict.get('model', state_dict)
#     state_dict = state_dict.get('module', state_dict)
#     # beit v2 didn't strip module

#     out_dict = {}
#     for k, v in state_dict.items():
#         if 'relative_position_index' in k:
#             continue
#         if 'patch_embed.proj.weight' in k:
#             O, I, H, W = model.patch_embed.proj.weight.shape
#             if v.shape[-1] != W or v.shape[-2] != H:
#                 v = resample_patch_embed(
#                     v,
#                     (H, W),
#                     interpolation=interpolation,
#                     antialias=antialias,
#                     verbose=True,
#                 )
#         elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
#             # To resize pos embedding when using model at different size from pretrained weights
#             num_prefix_tokens = 1
#             v = resample_abs_pos_embed(
#                 v,
#                 new_size=model.patch_embed.grid_size,
#                 num_prefix_tokens=num_prefix_tokens,
#                 interpolation=interpolation,
#                 antialias=antialias,
#                 verbose=True,
#             )
#         elif k.endswith('relative_position_bias_table'):
#             m = model.get_submodule(k[:-29])
#             if v.shape != m.relative_position_bias_table.shape or m.window_size[0] != m.window_size[1]:
#                 v = resize_rel_pos_bias_table(
#                     v,
#                     new_window_size=m.window_size,
#                     new_bias_shape=m.relative_position_bias_table.shape,
#                 )
#         out_dict[k] = v
#     return out_dict


# def _create_beit(variant, pretrained=False, **kwargs):
#     out_indices = kwargs.pop('out_indices', 3)
#     model = build_model_with_cfg(
#         Beit, variant, pretrained,
#         pretrained_filter_fn=checkpoint_filter_fn,
#         feature_cfg=dict(out_indices=out_indices, feature_cls='getter'),
#         **kwargs,
#     )
#     return model


# @register_model
# def beit_base_patch16_224(pretrained=False, **kwargs) -> Beit:
#     model_args = dict(
#         patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
#         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1)
#     model = _create_beit('beit_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
#     return model


# @register_model
# def beit_base_patch16_384(pretrained=False, **kwargs) -> Beit:
#     model_args = dict(
#         img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12,
#         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1)
#     model = _create_beit('beit_base_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs))
#     return model


# @register_model
# def beit_large_patch16_224(pretrained=False, **kwargs) -> Beit:
#     model_args = dict(
#         patch_size=16, embed_dim=1024, depth=24, num_heads=16,
#         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
#     model = _create_beit('beit_large_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
#     return model


# @register_model
# def beit_large_patch16_384(pretrained=False, **kwargs) -> Beit:
#     model_args = dict(
#         img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16,
#         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
#     model = _create_beit('beit_large_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs))
#     return model


# @register_model
# def beit_large_patch16_512(pretrained=False, **kwargs) -> Beit:
#     model_args = dict(
#         img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16,
#         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
#     model = _create_beit('beit_large_patch16_512', pretrained=pretrained, **dict(model_args, **kwargs))
#     return model


# @register_model
# def beitv2_base_patch16_224(pretrained=False, **kwargs) -> Beit:
#     model_args = dict(
#         patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
#         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
#     model = _create_beit('beitv2_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
#     return model


# @register_model
# def beitv2_large_patch16_224(pretrained=False, **kwargs) -> Beit:
#     model_args = dict(
#         patch_size=16, embed_dim=1024, depth=24, num_heads=16,
#         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
#     model = _create_beit('beitv2_large_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
#     return model
